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Manager, Perflab Data Science Team @ Nvidia
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Moscow Institute of Physics and Technology (State University) (MIPT)
Master's degree, Applied Mathematics and Physics
January 1, 2015 – January 1, 2017
Skolkovo Institute of Science and Technology
Master's degree, IT
January 1, 2015 – January 1, 2017
Moscow Institute of Physics and Technology (State University) (MIPT)
Bachelor's degree, Applied Mathematics and Physics
January 1, 2011 – January 1, 2015
NVIDIA
Manager, Perflab Data Science Team
April 1, 2024 – Present
NVIDIA
Manager, Perflab Data Science Team
April 1, 2022 – April 1, 2024
NVIDIA
Manager, Moscow Data Science team
May 1, 2020 – April 1, 2022
NVIDIA
Deep learning engineer
July 1, 2017 – May 1, 2020
ETH Zürich
Visiting Master Student
October 1, 2016 – January 1, 2017
RoboCV
Research engineer (intern)
June 1, 2016 – August 1, 2016
Eldorado LLC
Data Analysis Specialist in the Department of Financial Planning and Sales Control
March 1, 2016 – May 1, 2016
Skoltech computer vision group
Master Student
February 1, 2016 – May 1, 2017
Institute for Problems of Information Transmission
Researcher
March 1, 2015 – August 1, 2015
Fusion of LIDAR and image data for pedestrian detection
May 1, 2016 – Present
Brute force sliding window approach for object detection is logical but not computationally efficient way to go if we want to detect objects fast. The way to avoid it - use proposals generator, which selects orders of magnitude less regions after some preprocessing. The aim of the project was to build the architecture which would generate proposals using the LIDAR data and then detect pedestrians using pre-trained CNN. Our contribution is the way of handling proposals obtained from LIDAR point cloud. Unlike the established way of considering bounding boxes on the image, we look at the projections of LIDAR points onto the feature maps, obtained via CNN.
Smart Waste collection
December 1, 2015 – Present
Problem There is a number of waste containers in different parts of city. The task is to make the optimal month schedule for truck which unloads them. This schedule should use predicted from history data for filling speed of each container and minimize costs for used fuel. This project was proposed by founders of Chistoe Delo MIPT, who also provided data from 11 containers already installed in Dolgoprudny. Outcomes ► Formulated this problem as a linear programming optimization problem ► Solved it with both Gurobi solver and our "greedy" approach ► Created schedule of routes for 20-60 days with up to 100 containers distributed through Moscow ► Created visualization of routes using Yandex.Maps API
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory at NVIDIA, moving from Deep Learning Engineer to Manager, indicates a strong fit for a structured, research-intensive, and performance-driven environment. The personal projects demonstrate initiative and a proactive approach to problem-solving. The academic background from top-tier institutions further supports a culture of continuous learning and high achievement. However, the projects are heavily focused on research and data science, which might require adaptation if the target role is purely software engineering with less emphasis on ML/AI research.
Soft Skills & Operational Fit
The candidate's experience as a Manager at NVIDIA suggests strong leadership, project management, and potentially team collaboration skills. The project descriptions indicate problem-solving abilities and a structured approach to complex challenges. However, without specific psychometric test results, a detailed assessment of stress handling or work attitude is not possible.